21 citations · 69 across the 10 of their papers we have counts for
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Uniform Error and Posterior Variance Bounds for Gaussian Process Regression with Application to Safe Control
Armin Lederer, Jonas Umlauft, Sandra Hirche
In application areas where data generation is expensive, Gaussian processes are a preferred supervised learning model due to their high data-efficiency. Particularly in model-based…
Deep Learning based Uncertainty Decomposition for Real-time Control
Neha Das, Jonas Umlauft, Armin Lederer +2
Data-driven control in unknown environments requires a clear understanding of the involved uncertainties for ensuring safety and efficient exploration. While aleatoric uncertainty…
Localized active learning of Gaussian process state space models
Alexandre Capone, Jonas Umlauft, Thomas Beckers +2
The performance of learning-based control techniques crucially depends on how effectively the system is explored. While most exploration techniques aim to achieve a globally accura…
Posterior Variance Analysis of Gaussian Processes with Application to Average Learning Curves
Armin Lederer, Jonas Umlauft, Sandra Hirche
The posterior variance of Gaussian processes is a valuable measure of the learning error which is exploited in various applications such as safe reinforcement learning and control…
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control
Armin Lederer, Jonas Umlauft, Sandra Hirche
Data-driven models are subject to model errors due to limited and noisy training data. Key to the application of such models in safety-critical domains is the quantification of the…
Mean Square Prediction Error of Misspecified Gaussian Process Models
Thomas Beckers, Jonas Umlauft, Sandra Hirche
Nonparametric modeling approaches show very promising results in the area of system identification and control. A naturally provided model confidence is highly relevant for system-…